File set-up

Set working directory to current directory

if (rstudioapi::isAvailable()) {
  setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
}

Loads standard libraries and resolve conflicts

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(conflicted)
conflict_prefer("filter", "dplyr")
## [conflicted] Will prefer dplyr::filter over any other package.
conflict_prefer("select", "dplyr")
## [conflicted] Will prefer dplyr::select over any other package.
conflict_prefer("slice", "dplyr")
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conflict_prefer("rename", "dplyr")
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conflict_prefer('intersect', 'dplyr')
## [conflicted] Will prefer dplyr::intersect over any other package.

Load specific libraries

library(gplots)
library(ggpubr)
library("quantreg") 
## Loading required package: SparseM
## 
## Attaching package: 'SparseM'
## The following object is masked from 'package:base':
## 
##     backsolve
library(ggrepel)

Read data

all_circ = read_tsv('../data/Supplementary_Table_2_all_circRNAs.txt')
## Rows: 1137099 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (16): chr, strand, cell_line, tool, circ_id, circ_id_strand, count_group...
## dbl  (7): start, end, BSJ_count, n_detected, n_db, estim_len_in, BSJ_count_m...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
val = read_tsv('../data/Supplementary_Table_6A_precision_values.txt')
## Rows: 29 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr  (2): tool, count_group
## dbl (16): nr_qPCR_total, nr_qPCR_fail, nr_qPCR_val, nr_RR_total, nr_RR_fail,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
all_circ
val

Two tools

only for circRNAs in BSJ count group ≥ 5

combo = tibble()


for (tool_1 in all_circ %>% pull(tool) %>% unique()) {
  for (tool_2 in all_circ %>% pull(tool) %>% unique()) {
    for (cell in all_circ %>% pull(cell_line) %>% unique()) {
      nr_union = all_circ %>%
        filter(tool %in% c(tool_1, tool_2),
               count_group == 'count ≥ 5',
               cell_line == cell) %>%
        select(circ_id) %>%
        unique() %>% nrow()

      nr_intersection = all_circ %>%
        filter(tool == tool_1,
               count_group == 'count ≥ 5',
               cell_line == cell) %>%
        select(circ_id) %>%
        inner_join(all_circ %>%
                     filter(tool == tool_2,
                            count_group == 'count ≥ 5',
                            cell_line == cell) %>%
                     select(circ_id)) %>%
        unique() %>% nrow()

      combo = combo %>%
        bind_rows(tibble(tool_1, tool_2, cell, nr_union, nr_intersection))

    }
  }
}
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## Warning in inner_join(., all_circ %>% filter(tool == tool_2, count_group == : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 717 of `x` matches multiple rows in `y`.
## ℹ Row 717 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.
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combo

clean up dataframes and add validation rates

combo = combo %>%
  rename(cell_line = cell) %>%
  left_join(val %>%
              filter(count_group == 'count ≥ 5') %>%
              rename(tool_1 = tool, perc_compound_val_1 = perc_compound_val) %>%
              select(tool_1, perc_compound_val_1, count_group)) %>%
  left_join(val %>%
              filter(count_group == 'count ≥ 5') %>%
              rename(tool_2 = tool, perc_compound_val_2 = perc_compound_val) %>%
              select(tool_2, perc_compound_val_2, count_group))
## Joining with `by = join_by(tool_1)`
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add total nr of circ per tool

combo = combo %>%
  left_join(all_circ %>%
              filter(count_group == 'count ≥ 5') %>%
              group_by(cell_line, tool) %>%
              select(circ_id, cell_line, tool) %>%
              unique() %>%
              summarise(total_n = n()) %>% ungroup() %>%
              rename(tool_1 = tool, total_n_1 = total_n)) %>%
  left_join(all_circ %>%
              filter(count_group == 'count ≥ 5') %>%
              group_by(cell_line, tool) %>%
              select(circ_id, cell_line, tool) %>%
              unique() %>%
              summarise(total_n = n()) %>% ungroup() %>%
              rename(tool_2 = tool, total_n_2 = total_n)) %>%
  mutate(w_val_rate = ((perc_compound_val_1 * total_n_1) + (perc_compound_val_2 * total_n_2)) /
           (total_n_1 + total_n_2)) %>%
  filter(!is.na(perc_compound_val_1), !is.na(perc_compound_val_2))
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## `.groups` argument.
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## `summarise()` has grouped output by 'cell_line'. You can override using the
## `.groups` argument.
## Joining with `by = join_by(tool_2, cell_line)`
combo

add tool letters

combo = combo %>%
  left_join(read_tsv('../data/details/tool_annotation.txt') %>%
              select(tool, tool_lt) %>%
              rename(tool_1 = tool, tool_lt_1 = tool_lt)) %>%
  left_join(read_tsv('../data/details/tool_annotation.txt') %>%
              select(tool, tool_lt) %>%
              rename(tool_2 = tool, tool_lt_2 = tool_lt)) %>%
  mutate(tool_combo = paste(tool_lt_1, tool_lt_2, sep = ""),
         tool_combo = map_chr(str_split(tool_combo, ""), ~str_c(str_sort(unique(.x)), collapse = "")))
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## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (7): tool, approach, tool_lt, lin_annotation, strand_anno, splicing, BSJ...
## 
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## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Joining with `by = join_by(tool_2)`
combo

add total nr of circ for that cell line and calculate perc increase

total_cl = all_circ %>%
  group_by(cell_line) %>%
  filter(count_group == 'count ≥ 5') %>%
  select(circ_id, cell_line) %>%
  unique() %>%
  count(cell_line) %>%
  rename(total_cell_line = n) %>% ungroup()

total_cl
combo = combo %>%
  left_join(total_cl) %>%
  mutate(perc_increase_t1  = (nr_union - total_n_1)/total_n_1)
## Joining with `by = join_by(cell_line)`
combo

save as a dataframe for Supplementary table 5

combo %>% write_tsv('../data/Supplementary_Table_7_combo_2tools.txt')

Generate a top 10 list of combinations (Supplementary Table 9)

remove the combinations of twice the same tool and only have one line per combo

combo_top = combo %>%
  filter(!tool_1 == tool_2) %>%
  filter(tool_1 < tool_2)

take all cell lines together and save as separate columns

combo_top = combo_top %>%
  select(tool_1, tool_2, nr_union, cell_line) %>%
  pivot_wider(names_from = cell_line, values_from = nr_union) %>%
  rename(nr_union_HLF = HLF, nr_union_NCI_H23 = `NCI-H23`, nr_union_SW480 = SW480) %>%
  left_join(combo_top %>%
              select(tool_1, tool_2, w_val_rate, cell_line) %>%
              pivot_wider(names_from = cell_line, values_from = w_val_rate) %>%
              rename(w_precision_HLF = HLF, w_precision_NCI_H23 = `NCI-H23`, w_precision_SW480 = SW480))
## Joining with `by = join_by(tool_1, tool_2)`

generate a rank for each metric

combo_top = combo_top %>%
  mutate(w_precision_HLF_rank = dense_rank(desc(w_precision_HLF)),
         nr_union_HLF_rank = dense_rank(desc(nr_union_HLF)),
         w_precision_NCI_H23_rank = dense_rank(desc(w_precision_NCI_H23)),
         nr_union_NCI_H23_rank = dense_rank(desc(nr_union_NCI_H23)),
         w_precision_SW480_rank = dense_rank(desc(w_precision_SW480)),
         nr_union_SW480_rank = dense_rank(desc(nr_union_SW480)))

select the top 5 in each category

combo_top = combo_top %>%
  slice_max(w_precision_HLF, n = 5) %>%
  bind_rows(combo_top %>% slice_max(nr_union_HLF, n = 5)) %>%
  bind_rows(combo_top %>% slice_max(w_precision_NCI_H23, n = 5)) %>%
  bind_rows(combo_top %>% slice_max(nr_union_NCI_H23, n = 5)) %>%
  bind_rows(combo_top %>% slice_max(w_precision_SW480, n = 5)) %>%
  bind_rows(combo_top %>% slice_max(nr_union_SW480, n = 5)) %>%
  unique()

combo_top

add tool annotation

tool_annotation = read_tsv('../data/details/tool_annotation.txt')
## Rows: 16 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (7): tool, approach, tool_lt, lin_annotation, strand_anno, splicing, BSJ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
tool_annotation
combo_top = combo_top %>%
  left_join(tool_annotation %>%
              rename(tool_1 = tool, approach_1 = approach, lin_annotation_1 = lin_annotation,
                     strand_anno_1 = strand_anno, splicing_1 = splicing, BSJ_filter_1 = BSJ_filter) %>%
              select(-tool_lt)) %>%
  left_join(tool_annotation %>%
              rename(tool_2 = tool, approach_2 = approach, lin_annotation_2 = lin_annotation,
                     strand_anno_2 = strand_anno, splicing_2 = splicing, BSJ_filter_2 = BSJ_filter) %>%
              select(-tool_lt))
## Joining with `by = join_by(tool_1)`
## Joining with `by = join_by(tool_2)`
combo_top

save as sup table 9

combo_top %>%
  select("tool_1", "tool_2", "nr_union_HLF", "nr_union_HLF_rank", "nr_union_NCI_H23", 
         "nr_union_NCI_H23_rank", "nr_union_SW480", "nr_union_SW480_rank" , "w_precision_HLF", 
         "w_precision_HLF_rank",  "w_precision_NCI_H23", "w_precision_NCI_H23_rank",
         "w_precision_SW480", "w_precision_SW480_rank", "approach_1", "approach_2",
         "lin_annotation_1", "lin_annotation_2", "strand_anno_1", "strand_anno_2",
         "splicing_1", "splicing_2", "BSJ_filter_1", 'BSJ_filter_2') %>% 
  arrange(tool_1, tool_2) %>%
  write_tsv('../data/Supplementary_Table_9_top_tool_combinations.txt')

Three tools

only for circRNAs in BSJ count group ≥ 5

combo_3 = tibble()

for (cell in all_circ %>% pull(cell_line) %>% unique()) {
  for (tool_1 in all_circ %>% pull(tool) %>% unique()) {
    for (tool_2 in all_circ %>% pull(tool) %>% unique()) {
      for (tool_3 in all_circ %>% pull(tool) %>% unique()) {
        nr_union = all_circ %>%
          filter(count_group == 'count ≥ 5') %>%
          filter(tool %in% c(tool_1, tool_2, tool_3)) %>%
          filter(cell_line == cell) %>%
          select(circ_id) %>%
          unique() %>% nrow()

        combo_3 = combo_3 %>%
          bind_rows(tibble(tool_1, tool_2, tool_3, nr_union, cell))
      }
    }
  }
}

combo_3

clean up dataframe and add validation rates

combo_3 = combo_3 %>%
  rename(cell_line = cell) %>%
  left_join(val %>%
              filter(count_group == 'count ≥ 5') %>%
              rename(tool_1 = tool, perc_compound_val_1 = perc_compound_val) %>%
              select(tool_1, perc_compound_val_1, count_group)) %>%
  left_join(val %>%
              filter(count_group == 'count ≥ 5') %>%
              rename(tool_2 = tool, perc_compound_val_2 = perc_compound_val) %>%
              select(tool_2, perc_compound_val_2, count_group)) %>%
  left_join(val %>%
              filter(count_group == 'count ≥ 5') %>%
              rename(tool_3 = tool, perc_compound_val_3 = perc_compound_val) %>%
              select(tool_3, perc_compound_val_3, count_group))
## Joining with `by = join_by(tool_1)`
## Joining with `by = join_by(tool_2, count_group)`
## Joining with `by = join_by(tool_3, count_group)`

add nr of circ from each group

combo_3 = combo_3 %>%
  left_join(all_circ %>%
              filter(count_group == 'count ≥ 5') %>%
              group_by(cell_line, tool) %>%
              select(circ_id, cell_line, tool) %>%
              unique() %>%
              summarise(total_n = n()) %>% ungroup() %>%
              rename(tool_1 = tool, total_n_1 = total_n)) %>%
  left_join(all_circ %>%
              filter(count_group == 'count ≥ 5') %>%
              group_by(cell_line, tool) %>%
              select(circ_id, cell_line, tool) %>%
              unique() %>%
              summarise(total_n = n()) %>% ungroup() %>%
              rename(tool_2 = tool, total_n_2 = total_n)) %>%
  left_join(all_circ %>%
              filter(count_group == 'count ≥ 5') %>%
              group_by(cell_line, tool) %>%
              select(circ_id, cell_line, tool) %>%
              unique() %>%
              summarise(total_n = n()) %>% ungroup() %>%
              rename(tool_3 = tool, total_n_3 = total_n)) %>%
  # not exactly correct for tools that are combo's of two tools, but will be removed
  mutate(w_val_rate = ((perc_compound_val_1 * total_n_1) + (perc_compound_val_2 * total_n_2) +
                         (perc_compound_val_3 * total_n_3))/(total_n_1 + total_n_2 + total_n_3))
## `summarise()` has grouped output by 'cell_line'. You can override using the
## `.groups` argument.
## Joining with `by = join_by(tool_1, cell_line)`
## `summarise()` has grouped output by 'cell_line'. You can override using the
## `.groups` argument.
## Joining with `by = join_by(tool_2, cell_line)`
## `summarise()` has grouped output by 'cell_line'. You can override using the
## `.groups` argument.
## Joining with `by = join_by(tool_3, cell_line)`
combo_3

add tool letters

combo_3 = combo_3 %>%
  left_join(read_tsv('../data/details/tool_annotation.txt') %>%
              select(tool, tool_lt) %>%
              rename(tool_1 = tool, tool_lt_1 = tool_lt)) %>%
  left_join(read_tsv('../data/details/tool_annotation.txt') %>%
              select(tool, tool_lt) %>%
              rename(tool_2 = tool, tool_lt_2 = tool_lt)) %>%
  left_join(read_tsv('../data/details/tool_annotation.txt') %>%
              select(tool, tool_lt) %>%
              rename(tool_3 = tool, tool_lt_3 = tool_lt)) %>%
  mutate(tool_combo = paste(tool_lt_1, tool_lt_2, tool_lt_3, sep = ""),
         tool_combo = map_chr(str_split(tool_combo, ""), ~str_c(str_sort(unique(.x)), collapse = "")))
## Rows: 16 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (7): tool, approach, tool_lt, lin_annotation, strand_anno, splicing, BSJ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Joining with `by = join_by(tool_1)`
## Rows: 16 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (7): tool, approach, tool_lt, lin_annotation, strand_anno, splicing, BSJ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Joining with `by = join_by(tool_2)`
## Rows: 16 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (7): tool, approach, tool_lt, lin_annotation, strand_anno, splicing, BSJ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Joining with `by = join_by(tool_3)`
combo_3

remove combo’s of two tools

combo_3 = combo_3 %>%
  filter(!nchar(tool_combo) == 2)

add total nr of circ for that cell line and calculate perc increase

combo_3 = combo_3 %>%
  left_join(total_cl) %>%
  mutate(perc_increase_t1 = (nr_union - total_n_1)/total_n_1)
## Joining with `by = join_by(cell_line)`

save as a dataframe for figures

combo_3 %>% write_tsv('../data/Supplementary_Table_8_combo_3tools.txt')